Register | Login

Asian Journal of Research in Computer Science

  • About
    • About the Journal
    • Submissions & Author Guideline
    • Accepted Papers
    • Editorial Policy
    • Editorial Board Members
    • Reviewers
    • Printed Hard copy
    • Subscription
    • Membership
    • Publication Ethics and Malpractice Statement
    • Digital Archiving
    • Contact
  • Archives
  • Indexing
  • Publication Charge
  • Submission
  • Testimonials
  • Announcements
Advanced Search
  1. Home
  2. Archives
  3. 2023 - Volume 15 [Issue 3]
  4. Original Research Article

Author Guidelines


Submit Manuscript


Editorial Board Member


Membership


Subscription


Classification and Segmentation of Brain Tumor Using EfficientNet-B7 and U-Net

  •   Antonius Fajar Adinegoro
  •   Gusti Ngurah Sutapa
  •   Anak Agung Ngurah Gunawan
  •   Ni Kadek Nova Anggarani
  •   Putu Suardana
  •   I. Gde Antha Kasmawan

Asian Journal of Research in Computer Science, Volume 15, Issue 3, Page 1-9
DOI: 10.9734/ajrcos/2023/v15i3320
Published: 10 March 2023

  • View Article
  • Download
  • Cite
  • References
  • Statistics
  • Share

Abstract


Tumors are caused by uncontrolled growth of abnormal cells. Magnetic Resonance Imaging (MRI) is modality that is widely used to produce highly detailed brain images. In addition, a surgical biopsy of the suspected tissue (tumor) is required to obtain more information about the type of tumor. Biopsy takes 10 to 15 days for laboratory testing. Based on a study conducted by Brady in 2016, errors in radiology practice are common, with an estimated daily error rate of 3-5%. Therefore, using the application of artificial intelligence, is expected to simplify and improve the accuracy of doctor's diagnose.

Keywords:
  • Convolutional neural network
  • U-Net
  • EfficientNet-B7
  • machine learning
  • brain tumor
  • Full Article - PDF
  • Review History

How to Cite

Adinegoro, A. F., Sutapa, G. N., Gunawan, A. A. N., Anggarani, N. K. N., Suardana, P., & Kasmawan, I. G. A. (2023). Classification and Segmentation of Brain Tumor Using EfficientNet-B7 and U-Net. Asian Journal of Research in Computer Science, 15(3), 1–9. https://doi.org/10.9734/ajrcos/2023/v15i3320
  • ACM
  • ACS
  • APA
  • ABNT
  • Chicago
  • Harvard
  • IEEE
  • MLA
  • Turabian
  • Vancouver
  • Endnote/Zotero/Mendeley (RIS)
  • BibTeX

References

Narendran MP, Narendira Kumar VK, Somasundaram DK. 3D brain tumors and internal brain structures segmentation in Mr Images. IJIGSP. 2012;4(1):35-43. DOI: 10.5815/ijigsp.2012.01.05

Alqudah AM. Brain tumor classification using deep learning technique - A comparison between cropped, uncropped, and segmented lesion images with different sizes. IJATCSE. 2019;8(6):3684-91. DOI: 10.30534/ijatcse/2019/155862019

Rachmad RA, Wahyu B, Purbaningtyas R. Klasifikasi Tumor Otak Menggunakan Convolutional Neural Network dengan Arsitektur EfficientNet-B3. J Sist Inf Teknol Inf Komputer. 2021;11(3):55-9.

Brady AP. Error and discrepancy in radiology: inevitable or avoidable? Insights Imaging. 2017;8(1):171-82. DOI: 10.1007/s13244-016-0534-1, PMID 27928712.

Varoquaux G, Cheplygina V. Machine Learning for Medical Imaging: Methodological Failures and recommendations for the future. NPJ Digit Med. 2022;5(1):48. DOI: 10.1038/s41746-022-00592-y, PMID 35413988.

Erickson BJ, Korfiatis P, Akkus Z, Kline TL. Machine learning for medical imaging. RadioGraphics. 2017;37(2):505-15. DOI: 10.1148/rg.2017160130, PMID 28212054.

Putra WSE, Wijaya AY, Soelaiman R. Klasifikasi Citra menggunakan convolutional neural network (CNN) Pada Caltech 101. J Tekn ITS. 2016;5(1).

Cui S, Tseng HH, Pakela J, Ten Haken RK, El Naqa I. Introduction to machine and deep learning for medical physicists. Med Phys. 2020;47(5):e127-47. DOI: 10.1002/mp.14140, PMID 32418339.

Goodfellow I, Bengio Y, Courville A. Deep learning. The MIT Press; 2016.

Team K. ’Keras documentation: Image Classification via fine-tuning with EfficientNet,’ Keras; 2020 [online].

Available:https://keras.io/examples/vision/image_classification_efficientnet_fine_tuning/

Tan M, Le Q. EfficientNet: Rethinking model scaling for convolutional neural networks. In: Proceedings of the 36th international conference on machine learning. Proceedings of the machine learning research. 2019;97:6105-14.

Putra TA, Rufaida SI, Leu JS. Enhanced skin condition prediction through machine learning using dynamic training and testing augmentation. IEEE Access. 2020;8: 40536-46. DOI: 10.1109/ACCESS.2020.2976045

Ibrahim Khalil MI, Tehsin S, Humayun M, Jhanjhi NZ, AlZain MA. Multi-scale network for thoracic organs segmentation. Comput Mater Continua. 2022;70(2):3251-65. DOI: 10.32604/cmc.2022.020561

Ronneberger O, Fischer P, Brox T. U-net: Convolutional networks for biomedical image segmentation. Lect Notes Comput Sci. 2015:234-41. DOI: 10.1007/978-3-319-24574-4_28

Lui MS, Wijaya EK, Hidayat M. Segmentasi citra Hewan dengan convolutional neural network Arsitektur U-NET; 2014. [online] [cited Feb 20, 2023]. Available:http://yuita.lecture.ub.ac.id/files/2021/12/Tugas-4-Kelompok-8.pdf

Nickparvar M. Brain tumor MRI dataset. [Data set]. Kaggle. K. M. Schmainda, M. Prah. Data from Brain-Tumor-Progression. The Cancer Imaging Archive; 2018. DOI: 10.34740/KAGGLE/DSV/2645886,

Available:https://doi.org/10.7937/K9/TCIA.2018.15quzvnb

Cheng J, Huang W, Cao S, Yang R, Yang W, Yun Z, et al. Enhanced performance of Brain Tumor Classification via tumor region augmentation and partition. PLOS ONE. 2015;10(10):e0140381. DOI: 10.1371/journal.pone.0140381, PMID 26447861.

Schmainda KM, Prah M. Data from brain-tumor-progression. The Cancer Imaging Arch; 2018. DOI: 10.7937/K9/TCIA.2018.15quzvnb

Pedano N, Flanders AE, Scarpace L, Mikkelsen T, Eschbacher JM, Hermes B, et al. The cancer genome atlas low grade glioma collection (TCGA-LGG) (version 3) [Data set]. The Cancer Imaging Arch; 2016. DOI: 10.7937/K9/TCIA.2016.L4LTD3TK

  • Abstract View: 25 times
    PDF Download: 27 times

Download Statistics

Downloads

Download data is not yet available.
  • Linkedin
  • Twitter
  • Facebook
  • WhatsApp
  • Telegram
Make a Submission

Information

  • For Readers
  • For Authors
  • For Librarians

Current Issue

  • Atom logo
  • RSS2 logo
  • RSS1 logo


Copyright © 2010 - 2023 Asian Journal of Research in Computer Science. All rights reserved.